In this paper, we investigate, for SER tasks, the use of such speech and/or textual representations computed by models pre-trained through self-supervised learning.
Since these already existing pre-trained models were initially designed for speech recognition ASR [1]} or natural language understanding [2]}, it is not obvious that they are also relevant for SER. For instance, at the acoustic level ASR tends to focus on phone level that lasts about 30 ms while emotions are usually supported on about 1 s of speech.
